Earticle

Purpose: This study aimed to analyze the stress distribution and deformation in implant abutments made from titanium (Ti-6Al-4V), zirconia, and polyetheretherketone (PEEK), including their screws and fixtures, under various loading conditions using finite element analysis (FEA). Methods: Three-dimensional models of the mandible with implant abutments were created using Siemens NX software (NX10.0.0.24, Siemens). FEA was conducted using Abaqus to simulate occlusal loads and assess stress distribution and deformation. Material properties such as Young’s modulus and Poisson’s ratio were assigned to each component based on literature and experimental data. Results: The FEA results revealed distinct stress distribution patterns among the materials. Titanium alloy abutments exhibited the highest stress resistance and the most uniform stress distribution, making them highly suitable for long-term stability. Zirconia abutments showed strong mechanical properties with higher stress concentration, indicating potential vulnerability to fracture despite their aesthetic advantages. PEEK abutments demonstrated the least stress resistance and higher deformation compared to other abutment materials, but offered superior shock absorption, though they posed a higher risk of mechanical failure under high load conditions. Conclusion: The study emphasizes the importance of selecting appropriate materials for dental implants. Titanium offers durability and uniform stress distribution, making it highly suitable for long-term stability. Zirconia provides aesthetic benefits but has a higher risk of fracture compared to titanium. PEEK excels in shock absorption but has a higher risk of mechanical failure compared to both titanium and zirconia. These insights can guide improved implant designs and material choices for various clinical needs.

Purpose: To evaluate the three-dimensional trueness of upper and lower denture bases produced using a digital light processing (DLP) printer and immersed in a constant-temperature water bath. Methods: An edentulous model was prepared and fitted with denture bases and occlusal rims manufactured using base plate wax. After scanning the model, denture bases, and occlusal rims, complete denture designs were created. Using the designs and a DLP printer, 10 upper and 10 lower complete dentures were manufactured. Each denture was scanned before (impression surface of upper denture base before constant temperature water bath [UBC] and impression surface of lower denture base before constant temperature water bath [LBC] groups) and after (impression surface of upper denture base after constant temperature water bath [UAC] and impression surface of lower denture base after constant temperature water bath [LAC] groups) immersion in the constant-temperature water bath. Scanned files were analyzed by comparing reference and scanned data, with statistical analysis conducted using the Kruskal-Wallis test (α=0.05). Results: Statistical analysis revealed no significant differences between the UBC and LBC groups, nor between the UAC and LAC groups (p>0.05). However, significant differences were observed between the UBC and UAC groups and between the LBC and LAC groups, i.e., before and after the constant-temperature water bath for both maxillary and mandibular denture bases (p<0.05). Conclusion: Denture bases not immersed in the constant-temperature water bath (UBC and LBC groups) exhibited error values within 100 μm, whereas those immersed in the water bath (UAC and LAC groups) showed error values exceeding 100 μm.

Purpose: This study aimed to generate virtual mandibular left first molar teeth using deep convolutional generative adversarial networks (DCGANs) and analyze their matching accuracy with actual tooth morphology to propose a new paradigm for using medical data. Methods: Occlusal surface images of the mandibular left first molar scanned using a dental model scanner were analyzed using DCGANs. Overall, 100 training sets comprising 50 original and 50 background-removed images were created, thus generating 1,000 virtual teeth. These virtual teeth were classified based on the number of cusps and occlusal surface ratio, and subsequently, were analyzed for consistency by expert dental technicians over three rounds of examination. Statistical analysis was conducted using IBM SPSS Statistics ver. 23.0 (IBM), including intraclass correlation coefficient for intrarater reliability, one-way ANOVA, and Tukey’s post-hoc analysis. Results: Virtual mandibular left first molars exhibited high consistency in the occlusal surface ratio but varied in other criteria. Moreover, consistency was the highest in the occlusal buccal lingual criteria at 91.9%, whereas discrepancies were observed most in the occusal buccal cusp criteria at 85.5%. Significant differences were observed among all groups (p<0.05). Conclusion: Based on the classification of the virtually generated left mandibular first molar according to several criteria, DCGANs can generate virtual data highly similar to real data. Thus, subsequent research in the dental field, including the development of improved neural network structures, is necessary.

Purpose: The purpose of this study is to investigate the applications and prospects of big data in digital dental healthcare. Methods: The study included 30 participants in the dental field (dentists, technicians, professors, and graduate students). From June 25 to 30, 2023, the contents of the study were thoroughly explained, consent was obtained from the research subjects, and a questionnaire was administered via an internet service. The questionnaires of 28 participants who responded completely were used for analysis. The collected data were statistically processed using IBM SPSS Statistics ver. 22.0 (IBM). Results: The use of big data in digital dental healthcare, digital dental health system, mobile dental health, dental health analysis, and telehealthcare were all heavily surveyed, with an average score of 3.97 or higher on a 5-point Likert scale. The areas where big data can be utilized in digital dental healthcare are as follows. The utilization rate for three-dimensional digital product development via linkage with big data systems and industrial field manufacturing technology was found to be 4.11±0.67, and the analysis of trends by age in the occurrence of various oral diseases was found to be 4.00±0.98. Conclusion: In the future, research into the viability of big data’s success in the medical data field, which is directly related to human life, is needed. Additionally, social policies and regulations regarding big data–related information and standards in dental healthcare are necessary.

Purpose: This study aimed to investigate the effect of ego resilience and problem-solving ability on career identity. Methods: An online survey targeting dental technology students was conducted, and the responses of 206 students were analyzed using IBM SPSS Statistics ver. 21.0 (IBM). Descriptive statistics, independent samples t-test, one-way ANOVA (Duncan post-hoc analysis), and multiple regression analysis were performed. Results: The ego resilience-emotion control ability increased with career identity (β=0.129, p=0.007). The problem-solving and analysis ability, increased with the career identity-cognitive factor (β=0.535, p<0.001). In addition, career identity was observed to be high in male respondents (p=0.003) who were aged >23 years (p=0.002), in their 4th grade (p=0.023), had a grade of ≥4.0 (p=0.018), and hoped to get a job as a dental technician after graduation (p=0.001). Conclusion: Programs to establish the career identity of dental technology students are recommended to include contents that can improve cognitive abilities such as emotional regulation ability and problem-solving ability among those with ego resilience.